This article is a continuation of an article we published in August 2020 - “Disruptive Technologies for Smart Cities – Cloud Computing” for the presentation of the interim results of the ongoing Erasmus+ project Smart technologies by design (Smart by Design). The article is based on the materials produced by the project partners GAIA & DEUSTO.
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Data Analytics is the approach that allows companies to analyse the data they generate in their activity enabling them to draw conclusions that affect their business. Better known as Big Data, companies manage this information in order to adopt strategies that will help them to improve their business turnover. Thus, it helps them improve operational efficiency, customer user experience and also allows them to improve their business models. All these data generated by companies in their activity is one of the concerns they have to face today. They should evaluate the importance of this information, what information they will have to store or even what part of all these data they can sell.
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Data analysis means the translation of information into opportunities for companies to take advantage of all these data (Schneider. 2017). This is why, “Data Analytics” is also called as a translator or business generator, because it allows to explore personalised solutions to carry out your projects. At present, information as services is a business model that is expanding wherein increasingly more businesses are seeking to monetise the information they obtain. According to the International Statistical Institute, businesses that use information will see their productivity increase by 430 billion dollars by 2020 in contrast with those that do not use it.
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Services offered by platforms related to information analysis is growing along with new solutions in terms of storage capacities as well as processing. Some of the platforms that currently exist are as follows:

• Hadoop
• Gridgain
• HPCC
• Storm
• Spark
• Hive
• Kafka
• Flume

​The first standard on big data was published in the end of 2015 by the International Telecommunication Union (ITU), hence, there are already international rules and standards. ITU-T Y.3600: provides requisites, capabilities and use cases of cloud computing based big data (Y.BigDatareqts, 2015).

Big Data when merged with Cloud Computing offers the ability to collect, store, analyse, visualise and handle large amounts of data, which cannot be analysed with traditional technologies (Iglesias. A, 2015).
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When we refer to data analysis, we can differentiate five key applications of such technologies: ​

1. Explore massive data or Big Data management. Information management is assumed to be one of the biggest challenges that the companies will be facing for best decision-making, operations improvement and risk reduction. To obtain a more complete view of customers. The companies have a greater number of information sources about their customers, which they manage to provide better and more personalised services, as well as to predict customer behaviour.

• Increase in security. Such technologies are used in order to prevent attacks by locating anomalies that may occur, by analysing patterns and threats. In this usage type, we can distinguish three applications:

• Improve intelligence and surveillance: with continued real time analysis to find patterns.
• Prevention of attacks: with network traffic analysis to deal with espionage, intrusiveness, cyber attacks…
• Prediction and prevention of cybercrime: by analysing telecommunications and social network data to analyse threats and to act before the criminals.

• Operations Analysis. Helps companies to make operational decisions, increasing their intelligence and efficiency. To do so, they can check the updated information with the different possible systems.
• Increase in data storage. Creation of new data storage structures.

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​The expected evolution is that the data volumes will continue to grow due to the expected increase in the number of networked devices. The future platforms will improve the ways in which data is analysed, while SQL will continue to be the standard, Spark is emerging as a complementary tool which will continue to grow.

New tools will be created to analyse without an analyst, companies such as Microsoft and Salesforce have announced such type of solutions. Programmes such as Kafka and Spark that allow to use these data in real time will also continue to be developed. According to many experts, it is thought that “fast data” and “actionable data” will replace big data. It is also expected that algorithm markets will emerge. Companies will begin to buy algorithms instead of programming them and add their own information (Logicalis, 2016). Although such type of solutions already exist, it is assumed that these will grow multi-fold.
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On the other hand, one of the challenges data analytics platforms will face is privacy, especially since the latest regulations made by the European Commission.
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With regard to standards, The Big Data Value Association (BDVA) is working to define standards of Big Data priorities and interoperability. The association has a team dedicated to this matter (Task Force 6) that, as of today, has already defined a reference model for Big Data.

​A workshop was held in Brussels in June 2017 to collaborate with other standardisation communities to create a roadmap for the harmonisation of Big Data standards. Representations from ETSI, AIOTI WG3, CEN/CENELEC, OASC, ISO/JTC1/WG9, W3C, OneM2M, Industry 4.0, European Commission, PPP based important Big Data projects among others, participated in the event. Follow-up activities took place in 2017 on the side-lines of the ISO IEC JTC1 WG9 Data Reference Architecture meeting held in Dublin.
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Information analysis has a large number of potential applications and areas of use (Marr.B, 2016):

• Continue working in customer segmentation
• Optimisation and understanding of business processes
• Monitoring and optimisation of business processes
• Improve public health systems
• Improve sport yields of citizens
• Improvements in science and innovation
• Optimise the performance of machinery of companies
• Improvement in security and support for the fulfilment of law
• Applications in Smart Cities related solutions
• Finance

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The article is a continuation of an article we published in June 2020 - “Disruptive Technologies for Smart Cities - Artificial Intelligence” for the presentation of the interim results of the ongoing Erasmus+ project Smart technologies by design (Smart by Design). The article is based on the materials produced by the project partners GAIA & DEUSTO.

Cloud Computing is a set of technologies that enables computational services over the network (usually the Internet). Namely, they are services accessible from any device connected to the network and allows access to applications, information and services without having to be installed on a hard drive. This type of technology enables the user to obtain total mobility as they can access their services, programmes and information from anywhere (Xhafa, F. et al., 2014).

There are many companies that offer “cloud” services ranging from storage of files to running of software programmes accessible over the network. Therefore, Cloud computing represents a significant change in how companies and public bodies process data, files and applications. It offers thousands of tools to users and it is a service which may be accessed by all in terms of cost as it is normally provided through licenses granted depending on the number of users in each company. It also has a high level of security provided by the service provider and allows to work in a team remotely. According to Forrester Research, the global cloud market has a compound annual growth rate of 22% and is expected to reach $146 billion by the end of 2017 and 236 billion by 2020.

• Amazon Elastic Compute Cloud (EC2)
• Windows Azure
• Google App Engine 
• Red Hat Openshift
• IBM SmartCloud
• VM Cloud Suite
• Openstack
• Github
• Heroku
• Salesforce
• Success Factors
• Twilio

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Cloud Computing is a relatively new technology and therefore there are several entities still developing standards and it is assumed that there is still a long way to go. Currently, there are two types of standards related to cloud computing: prescriptive (communications) and evaluative (systems quality) (Aguilar, P. et al., 2016).

In terms of ISO standards as of today the following exist (De Hert, P, et al., 2016):

• ISO 27018: best practices for the control of data protection for cloud computing services;
• ISO/IEC 27001 and ISO/IEC 27002: information security management aimed at cloud service providers. As mentioned before, there are several entities working on this issue. The Internet Engineering Task Force (IETF) establishes internet standards, which has so far created RFC 6208 that describe the media types for the Cloud Data Management interfaces. On the other hand, the International Telecommunication Union (ITU) assigned its teams to work on standardisation and cloud security.

These work groups have henceforth published several recommendations:

• ITU-T Y.3501: reference framework for Cloud Computing
• ITU-T Y.3510: infrastructure requirements
• ITU-T Y.3520: resource management of end users
• ITU-T Y.3511: inter-network and infrastructure communication
• ITU-T X.1600: security framework for Cloud Computing
• ITU-T Y.ccdef: Cloud Computing - overview and vocabulary
• ITU-T Y.ccra: Cloud Computing reference architecture

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Cloud computing has many potential applications and the most relevant are:

• Storage: to be able to access and share information (Mega, Google Drive, Amazon Cloud Drive…)
• Version control: to manage software repository projects (GitHub, Kiln, Springloops…)
• CRM: to increase sales and strengthen customer relationships (Microsoft Dynamics CRM Online, NetSuite CRM+, Maximizer CRM…)
• Email marketing: monitoring and sending email marketing campaigns (Constant contact, AWeber, eConnectMail…)
• Development environments: to create applications from any place (Cloud9 IDE, Codenvy, Koding…)
• Time management: manage work team time (Harvest, Toggl, DeskTime…)
• Project management: Clarizen, Genius Project, Daptiv…
• Help desk: Zendesk, Get Satisfaction, Desk.com...
• Usage metrics: (Flurry, Segment.io, FoxMetrics…)
• ​Application monitoring: appdynamics, Boundary, Compuware

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Cloud Computing is already a reality with a lot of companies that have implemented this technology or are in the process of doing so. But this is expected to continue to grow until almost all companies need these services so that they can continue with their activities.

There are three areas where Cloud Computing will continue to develop in order to continue to grow and to take advantage of all its benefits and opportunities:

1. Geographical location: higher quality connectivity must be ensured in order to ensure more efficient services.
2. Economic situation: as some companies are still reluctant to take the step.
3. Security: efforts should be made to ensure that companies believe that their data is safe on the cloud and trust the services
   
   

Despite these three drawbacks that should be overcome, ever-increasing number of companies and users will opt for Cloud Computing for all the benefits it offers. Areas not yet exploited till now will be developed, as increase in bandwidth and supplier capabilities will enable them to do so, such as:

• Games: instead of running games on the local computer, information will be sent to the cloud, and will be executed there without the need for any type of installation.
• Mobile web: similar to applications for PCs that are now mostly being developed, it is assumed that solutions will also be there for mobile environments.
  
  ​With regard to companies, according to most subject matter experts, all of them will use hybrid clouds to provide solutions to their problems, that is to say, private clouds for critical or sensitive information and public clouds for more generic solutions or for information that has no special relevance. Cloud environments will require greater agility, flexibility and speed in the IT departments. Therefore, professionals will not be able to work individually, they will have to collaborate with peers and third parties.

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There are many entities working on the standardisation of cloud computing. The Internet Engineering Task Force (IETF) is one among them which is working on this issue the most and has two new standards in draft phase:

1. The first presents a framework of reference for Cloud systems based on design of interoperable services and their integration;
   
2. The second presents a scenario of inter-operation between clouds offering transcoding and interoperability requirements affecting sending of multimedia content.

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Until now, three types of cloud based services were offered: Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), but henceforth, we will hear more of XaaS (Everything as a Service). Cloud computing involves a change in the processing of information and its management, enabling all types of companies to acquire features of larger firms.

Everything that until now required installation of expensive infrastructure only within the reach of a few, in the coming years will be accessible to all and will create new service models such as Big Data as a Service, Graphics as a Service, Desktop as a Service. As described before, specific applications will be created for the Gaming industry as well as for mobile devices.
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Author
KISMC
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Smart Cities are, by design, municipalities that address challenges via a process of digital transformation (DX); in fact, the mission of Smart Cities can be described as “outcomes-based digital transformation”. This means using new methods of innovation and creativity, and new sources of information to enhance experiences, increase sustainability and resilience, and improve financial and operational performance.

Information Technologies (IT) that use a combination of cloud, mobility, and data analytics have the power to provide new solutions to long-standing urban challenges and enable new experiences for residents and communities, visitors and tourists, and local businesses. (IDC white paper “Accelerating the Digital Transformation of Smart Cities and Smart Communities”, 19 December 2017. Posted in Intelligent / Smart Cities Solutions).

Here we have presented some good practices for the digital transformation of cities in Bulgaria, based on smart city technologies.
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The city of Sofia participated in the initiative Digital Cities Challenge of the European Commission. The result was the elaboration of the Digital transformation strategy for Sofia (DTSS): A platform for smart growth. DTSS defined an action plan and a series of actions that strengthen the ICT business ecosystem located in Sofia, enabling the development of innovative solutions for the digital transformation of the city.

​As for further implementation of the action plan the city signed a technical support agreement with the European Investment Bank for its realisation through concrete investment projects. There are some cases (projects), based on smart city technologies that have been identified for immediate implementation by the Smart City Roadmap. (Source: The digital transformation strategy for Sofia: A platform for smart growth; Digital Cities Challenge Initiative)
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• Distributed platform of urban data - creating a Data Lake – a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data lake will not only be used to store data from and for the municipality but also business, citizens, academia. After creating the data lake, we would expand with different modules used for analytics, visualisation, and modelling, thus creating a data hub.
  General goal – help stakeholders be more informed and facilitate evidence-based policy making.
• Sofia’s Digital twin (cyber-physical platform for decision-making optimisation) - digital twin – a digital profile of the physical city that helps to optimise its performance and can be used as a platform for planning and decision-making but also experimentation, and research and development.
  General goal – help decision makers and experts to better plan and make decisions about the development of the city.
• Online platform for services in schools - for a few years now there has been an operating system for acceptance and attendance in kindergartens. Expanding it to the public schools and building new functionalities that allow new e-services.
  General goal – improve the communication between student parents and schools.
•  Dashboard for real-time utilities consumption  - creation of e-utility services helping buildings' owners save energy, gas and water using smart sensors. Creation of a mobile app/website for following utility consumption in real time. Testing in 3-5 properties (manufacturing, administrative, residential, retail). 
  General goal – optimisation of utility costs.
• Development of utilities efficiency model - development of a single model for efficiency based on meteorological conditions – creating an online platform for data collection and analysis that helps utility companies to increase the efficiency of resource utilisation.
  General goal - increased efficiency and service quality
• Transport modelling - creation of a dynamic transport model of the city. To be used to test scenarios.
  General goal - better planning and mobility management in the city.
• Integrated Mobility Platform - creation of an integrated mobility platform that provides real time information about all types of transport and routes in the city.
  General goal - optimise mobility as a service  
• Neighbourhood car sharing - building a platform for shared electric cars for a certain number of neighbouring residential buildings.
  General goal - improve the urban environment.
• Digital and physical space for startups located in Sofia - development of new or customisation of an existing e-platform for startups and scaleups. Creation of an office space for consultations for founders. The team there would also be responsible for synchronising, supporting and developing existing initiatives engaged with inspiring entrepreneurial qualities and innovative thinking.
  General goal - promote entrepreneurial qualities and innovative thinking among young people, improve founders’ entrepreneurial skills, make it easier for startups and investors to connect.

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​The Sofia Smart City Marketplace is an initiative of Cluster Sofia Knowledge City to develop a platform for publishing, storing and open access to a database of validated or ready-to-validate product and technological innovations that can be used in the process of transforming the city into a smart city.

​It is designed for those local government officials responsible for the development of the smart city by providing them with a simplified process for finding information on the latest products, applications, technologies and technological solutions with which the municipal structures can cope more successfully. The platform is beta version https://smartcitymarketplace.eu/.
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The agenda of the smart city of Burgas supports the city's smart city planning and development. The city is now keen to create a structure to its ambitions and is developing a Smart City Roadmap that refines its strategic intent and prioritises future investment intentions accordingly.

​The smart city of Burgas integrates technology with infrastructure to enable urban development that is more intelligent, interconnected, and efficient. Because of its manageable size and other endowments, Burgas is the perfect “urban laboratory” in which to test smart applications in a relatively controlled environment. The municipality’s commitment to a considered programme of further interventions that will form the mosaic of its proposed Smart City Roadmap will only make it smarter. There are some examples from a smart city planning agenda:

The Smart Cities Information System is a knowledge platform to exchange data, experience and know-how and to collaborate on the creation of smart cities, providing a high quality of life for its citizens in a clean, energy efficient and climate friendly urban environment.

SCIS brings together project developers, cities, research institutions, industry, experts and citizens from across Europe. SCIS focuses on people and their stories – bringing to life best practices and lessons learned from smart projects. Through storytelling, SCIS portrays the “human element” of changing cities. It restores qualitative depth to inspire replication and, of course, to spread the knowledge of smart ideas and technologies - not only to a scientific community, but also to the broad public! 

​Launched with support from the European Commission, SCIS encompasses data, experience and stories collected from completed, ongoing and future projects.

Focusing on energy, mobility & transport and ICT, SCIS thus showcases solutions in the fields of energy-efficiency in buildings, energy system integration, sustainable energy solutions on district level, smart cities and communities and strategic sustainable urban planning. Projects in the scope of SCIS are mostly co-funded by the European Commission, for example: the 12 Horizon 2020 Smart Cities and Communities (SCC1) projects (such as Triangulum, Sharing Cities or Stardust), the 7th Framework Programme projects CELSIUS and City-zen, and many more!

SCIS therefore analyses project results and experiences to:

• establish best practices which will enable project developers and cities to learn and replicate.
• identify barriers and point out lessons learned, with the purpose of finding better solutions for technology implementations and policy development.
• provide recommendations to policy makers and policy actions needed to address market gaps.

This material is prepared for the needs of Output 1 of the SMART project and is based on the market study conducted by the Cluster Sofia Knowledge City in 2019, supported by some of the members of the SMART project team at that time.

By smart city technologies are meant those technologies that refer to the concept of a smart city, most often these are digital and/or data-based technologies that are applicable in the real conditions of the city and contribute to the city's coping with public problems or challenges. In smart cities, these technologies are used to develop "critical infrastructure" in the following areas: transport, water and waste management, construction, energy, security, education, health, and urban management.
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The following areas of application of these technologies are presented below. All of them are known cumulatively in most of the smart cities around the world, which are at different stages in the process of transformation. This list does not represent all areas of application at all and is only an introduction for a better understanding of the way and reasons for the penetration of these technologies.

• Autonomous vehicles - vehicles equipped with sensors and software to work alone; full self-management capability (level 4) is achieved when human intervention is not expected to take control at any time.
• Bicycle sharing - bicycles for public use, either in docking centers or as freely used, to provide an alternative to riding, public transport, and private bicycle ownership. This option can cover the first mile / last mile segment when public transport does not take a door-to-door journey.
• Car sharing - access to short-term use of cars without full ownership; can be bidirectional (station-based), unidirectional (free-floating), spot-to-spot, or partial.
• Congestion pricing - fees for using a personal car in certain areas, during peak demand, or both.
• Demand-based micro-transit - sharing services with fixed routes, fixed stops, or both, often complementing existing public transit routes. The algorithms use a historical search to determine routes, vehicle size, and travel frequency. May include seat reservation options.
• Payment by digital public transport - digital and contactless payment systems in public transport, which allow prepayment and faster upload. Includes smart cards and mobile payments.
• Electronic call (private and combined) - the real-time ordering of point-to-point transportation via a mobile device. Unified e-ringing involves the dynamic connection of individual journeys with compatible routes to increase vehicle utilization (ie local real-time search optimization).
• Integrated multimodal information - real-time information on price, time, and availability of transport options in many modes.
• Intelligent road signals - improving overall traffic by dynamically optimizing traffic lights and speed limits, leading to higher average road speeds and less frequent stopping and returning. Includes preferential light technology that prioritizes emergency vehicles, public buses, or both.
• Consolidation of the parcel load - online matching of the demand for supplies with the available supply of freight capacity. By making maximum use of vehicles, fewer trucks make more deliveries.
• Predictable maintenance of transport infrastructure - sensory monitoring of the condition of public transport and related infrastructure (such as rails, roads, and bridges) so that predictive maintenance can be performed before accidents and disruptions occur.
• Real-time public transport information - real-time arrival and departure information for modes of public transport, including informal bus systems.
• Real-time road navigation - real-time navigation tools for selecting driving routes, with signals for construction, detours, traffic jams, and accidents. This is especially true for those who drive alone or in a car.
• Smart mailboxes - boxes in a place where people can pick up packages using individual access codes sent to their mobile devices.
• Smart parking - systems that direct drivers directly to the available spaces; may affect demand through variable charges.

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• Leak detection and control - remote monitoring of the condition of the pipes with the help of sensors and control of the pump pressure to reduce or prevent water leakage. Early identification of leaks can lead to follow-up by relevant city departments and utilities.
• Smart irrigation - optimizing irrigation by analysing information such as local weather, soil conditions, plant species, etc. to eliminate unnecessary watering.
• Monitoring of water consumption - feedback (via a mobile application, e-mail, text, etc.) on the water consumption of the occupant in order to raise awareness and reduce consumption. Smart water meters allow utilities to measure consumption remotely, reducing labour costs for a manual meter reading. It also allows for dynamic pricing.
• Water quality monitoring - real-time water quality monitoring (in networks, rivers, oceans, etc.) through signals delivered to the public through channels such as a mobile application, e-mail, text or website. This warns the public to avoid consumption or contact with polluted water and to make cities and utilities follow the problem immediately.
• Digital tracking and payment for waste disposal - digital payment systems according to the volume of generated waste; includes feedback (via mobile app, email, text, etc.) provided to users to raise awareness and reduce waste.
• Optimization of the waste collection route - use of sensors in the waste containers to measure the volume of waste and direct the routes of waste trucks. This application restricts the travel of garbage trucks to bins with a small amount of waste.

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• Building automation systems - systems that optimize the use of energy and water in commercial and public buildings by using sensors and analysis to manually or automatically eliminate inefficiencies. Includes optimized lighting and HVAC, as well as features such as access/security control and parking information.
• Home energy automation systems - optimization of energy consumption from the home by using intelligent thermostats, programmable and remotely controlled electronic devices (smart home), and control of backup electricity.
• Tracking energy consumption in the home - tracking the consumption of electricity in homes with feedback provided to the consumer through a mobile application, e-mail, or text to raise consumer awareness and promote their protection. It also allows utilities to remotely measure electricity use.

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• Supply automation systems - various types of smart grid technologies, including FDIR, M&D, Volt / Var, and substation automation, to optimize energy efficiency and grid stability.
• Dynamic electricity pricing - dynamic adjustment of electricity prices to reduce electricity consumption and reduce electricity generation costs. By reducing peak consumption, cities can reduce the number of power plants that operate during peak hours.
• Intelligent street lamps - connected and equipped with sensors energy-saving street lights (including LED), which optimize brightness and reduce maintenance needs. Smart street lights can be equipped with speakers, shot sensors, and other features to improve functionality

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• Body cameras - audio, video or photographic recording systems commonly used by police officers to record incidents and police operations.
• Crowd management - technology for monitoring and, where necessary, guiding crowds to ensure safety.
• Data-based building inspections - use of data and analysis to focus inspections on the buildings with the highest potential risks (e.g. prioritization of commercial buildings for fire code inspections and homes for lead inspections).
• Disaster Early Warning Systems - technology designed to anticipate and mitigate the effects of natural disasters such as hurricanes, earthquakes, floods and forest fires.
• Emergency response optimization - the use of analyses and technologies to optimize the processing of emergency calls and field operations, such as the strategic deployment of emergency vehicles.
• Shot Detection - Acoustic surveillance technology that includes audio sensors to detect, locate and alert police agencies to real-time shooting incidents.
• Home security systems - security systems that monitor homes and alert users, emergency services, or both, to unusual activity.
• Personal alarm applications - applications that alert you to an emergency by alerting the Emergency Center, loved ones, or both. Devices (such as personal protective equipment, crash detectors, and fall warning systems) can transmit location and voice data.
• Predictable control - the use of big data and analysis (including social media monitoring) to predict more accurately where and when crimes are likely to occur. These systems are used to deploy police patrols and prevent prevention.
• Real-time crime mapping - a technology used by law enforcement to map, visualize and analyse crime incident models. Information gathering and intelligence serves as a management tool for the efficient allocation of resources and accountability among employees.
• Intelligent surveillance - intelligent monitoring to detect anomalies based on visual emissions, including face recognition, intelligent closed-circuit television systems and registration number recognition.

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• Personalized learning - the use of data from students to identify people who need extra attention or resources; the potential for adapting the learning environment for individual students.
• Online retraining programs - lifelong learning opportunities provided in digital format, especially to help people who are unemployed or at risk of becoming unemployed to acquire new skills.
• Local e-career centers - online platforms for publishing open positions and profiles of candidates; can use algorithms to match compatible candidates with available jobs.
• Reduce job search time and increase net new employment.

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• Public health interventions based on maternal and child health data - use of analyses to target highly targeted health interventions for at-risk groups (in this case, identification of pregnant and new mothers to conduct educational campaigns for and postnatal care).
• Public-based health interventions to improve sanitation and hygiene - use of analyses to target highly targeted interventions, such as understanding where to increase rainfall absorption capacity or collecting data on sewage leaks systems.
• Urgent aid alerts - technologies that alert passers-by trained in CPR so that victims of cardiac arrest receive prompt and urgent care.
• Monitoring of infectious diseases - collection, analysis and response to prevent the spread of infectious and epidemic diseases. Includes awareness and vaccination campaigns (eg for HIV / AIDS).
• Integrated patient flow management systems - real-time hardware and software solutions that provide visibility to where patients are in the system to improve hospital operations and coordinate use at the city or multi-site level.
• Lifestyle clothing - portable devices that collect data on lifestyle and activity indicators and inform the user; they can promote exercise or other aspects of a healthy lifestyle.
• Online care search and planning - tools that support the selection of providers and providers with financial and clinical transparency.
• Real-time air quality information - real-time sensors to detect and monitor the presence of air pollution (outdoor, indoor, or both). Individuals can view the information online or on a personal device and decide to change their behaviour accordingly.
• Remote patient monitoring - collection and transmission of patient data for analysis and intervention by the healthcare provider elsewhere (eg monitoring of vital signs or blood sugar). Includes drug adherence technologies that help patients take medications as recommended by their healthcare provider.
• Telemedicine - virtual interaction of the patient and the doctor through audio-visual technology

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• Obtaining licenses and permits for business digitally - a digitalized process (as an online portal) for companies to obtain licenses and permits for operation.
• Digital Tax Submission - a business channel to perform online tax filing.
• Obtaining permits for the use of land and buildings by digital means - digitalization and automation of the application process for permitting the use of land and construction, reducing the time for approval, and increasing transparency.
• Open database for the cadastre - a complete database for the plots in the city, open to the public; allows for a more efficient land market by creating transparency of available land and reducing the cost of registering plots.
• Peer-to-peer accommodation platforms - digital markets where individual owners can list and rent properties for short-term accommodation.
• Digital civil services - digitalization of state administrative services aimed at citizens, such as filing income tax, registering cars or applying for unemployment benefits.
• Local applications of civic engagement - public engagement in urban issues through digital applications. It may include reporting problems and maintenance needs (for example, reporting broken street lamps through an application), providing information on policy decisions, participating in digital urban initiatives (such as open data hackathons), and interacting with city authorities and social services departments. networks,
• Local communication platforms - websites or mobile applications that help people connect and potentially meet other people in their community. It can be used to find people with similar interests and hobbies, to connect with neighbours, etc

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The SMART project is aimed at developing new competencies of the SMEs managers to manage deep-tech businesses in one very fast-growing market - the smart cities. The penetration of so-called smart city technologies results in the creation of new markets and requirements for new skills. On the basis for the achievement of this purpose is laid the map of digital smart disruptions.
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This short article explains some important terms and the approach implemented in this project.
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The picture is worth a thousand words - Fred R. Barnard.
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The map of digital and smart disruptions provides an easy for understanding and learning overview and analysis of how digital and disruptive technologies (innovations) do shape the smart city concept. The map includes the results generated by the focus group and corrected later by the partners with the results from the research on the technologies advances, level of change and impact, performance, purpose, and fit.

The map is a two-dimensional cross factorial analysis matrix made with different instruments in diverse forms (Fig.1). 

The ultimate purpose of a map is to improve the scenario planning of businesses and the cities in the process of their transformation into smart cities and to point out the opportunities for involvement of the businesses in the process. So, this can be treated as a sample of an opportunity map for every city in the process of urban management and for every company in the process of innovation management.

In the case of the project SMART, the map will present the results of the studies and research of the project partners in a systematic and simplified way, based on:

1. the conclusion and definition of the smart city’ technologies that might be a source for disruptive innovations in the main city’s areas. Every identified technology can be studied in regard to the market, product/service, delivery methods, production model and business model.
   
   
2. the conclusion and presentation of what the main smart city’s areas are, where the process of transformation is carried out, what their needs are, barriers and stimulations for smart technologies implementations;
   
   
3. the level of penetration of the smart city’ technologies into the smart city’s areas - the less level of penetration the less-differentiated business opportunities can be generated for applications in the near future. On the other hand, more deep-tech smart city applications are possible in the cities with a higher level of penetration of such technologies;
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4. the opportunities for the businesses and impact of these technologies and their application for the business growth and the cities transformation. The map has a digital format and encompasses all potential companies including start-ups and corporates which work or would like to move to the smart city segment.

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The definition of the disruptive innovation given by Clayton Christensen is the innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market-leading firms, products, and alliances. The following benefits and changes coming from the disruptive innovations are identified:

• everything that could be digital will become digital, technology will be embedded widely in products and services in a near term;
• customers look for high levels of personalization and individual approach and service;
• subscription-based business models and pay-per-click are rising;
• shortened product life cycle - on the one hand, the pace at which new products are adopted (number of years until x % penetration has been reached) increases. On the other hand, product life cycles are shortening. With technology products even to three or six months. For SMEs that invest and develop digital innovations the revenue stream is generated by products launched within the previous years.
• the use is replacing possessions - very often customers prefer to use the product, rent it or pay only for the time if using it.

Following this definition, it is easy to presume that in general the “disruptive smart city technology” is any kind of emerging, advanced & digital (but not only) technology that can generate disruptive innovations creating benefits and many changes in the context of an urban (territorial) area and if this process is well managed it should result in a better life of the citizen.

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The mapping is a structured process, focused on a topic or construct of interest, involving input from many participants, that produces an interpretable pictorial view (concept map) of their ideas and concepts and how these are interrelated. The mapping helps partners to think more effectively as a group without losing their individuality. It helps the project group to manage the complexity of the vision on the smart city disruptive technologies without trivializing them or losing detail.

The mapping process is one of the portfolios of many other similar methods that management and social scientists have developed like brainstorming, brainwriting, nominal group techniques, focus groups, affinity mapping, Delphi techniques, facet theory, and qualitative text analysis. The mapping process is focused on the major shifts from the business perspective and how these changes will affect the growth.

It uses focus groups to understand and analyse the impact of smart city technologies and their application for business growth. The trends, types of technology, and levels of transformation are included in studying within the process. Consequently, the project partners have to study a framework of the following five dimensions of these technologies:

• market - customer segments, citizens’ participation, needs, behaviours, trends;
• products and services - user experience, brands, product features, functionalities;
• delivery methods - supply chains, delivery models for online and offline business;
• production model - co-creation, co-development, production technologies, facilities, software, hardware, HR, etc.;
• business model - how revenue and cost models look like, what partnerships are developed, how public-private partnerships work, and what best practices are in place in terms of innovation.

The study includes the level of impact and change of the emerging technologies - so, what does exist now and what is the current maturity level (according to Gartner).
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The process is placed in a focus group that is facilitated by the leader of the mapping process. A mapping process in a such group involves five steps that can take place in a period of time, planned for the output and depending on the project development situation.
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1. The first step is preparation of the focus group. There are three things to be done here. The leader of the mapping process identifies who the participants will be in the focus group. It is a relatively small group of participants from the stakeholders involved (8-10 persons). Then, the leader works with the participants to develop the focus for the project. In this case, the group is focused on defining what the smart city disruption technologies are and on choosing what challenges (problems) areas to map (prognosis) in all of the outcomes (impacts) they might expect to be seen as a result.
   
   
2. In the second step (generation step) the participants in the focus group develop a large set of statements that address this focus. They generate statements that describe the advanced technologies that are currently used in smart city areas and mark what is their level of penetration. They also generate statements describing specific outcomes that might occur as a result of the implementation of these technologies. A wide variety of methods can be used to accomplish this including traditional brainstorming, and so on.
   
   The group will generate finally many and diverse statements for possible current and future applications of smart disruptive technologies in smart cities, answering the following questions:
   - What of the city’s challenges (problems) do you know as already resolved based on the defined in the first stage smart city disruptive technologies?
   - What other challenges (problems) could be resolved with the smart city disruptive technologies in the cities of future?
   
   
3. In the third, the structuring step, the participants select and sort the statements into groups (clusters) of similar ones, and every participant rate each of the statements on some scale for their relative importance and level of penetration of the technologies in the respective areas, from the 1-to-5 scale. In this stage, the group can use the results from facts finding and studies that are already in place done by the project partners. The participants in the group can receive, use, and interpret the gathered reports, analyses, and the open access papers on emerging & smart disruptive technologies and their use before the final evaluation (scoring).
   
   
4. The fourth, the representation stage is where the analysis is done - this is the process of taking the sort and rating input and “representing” it in a matrix form. Every group of similar statements and received scoring on the level of penetration will be located in a proper and logically defined field of the matrix. So, every area in the result will be filled or empty. The empty fields will have to be analysed in-depth and the marked field will have a group of statements that well describe the current and future situation of the smart city disruptive technologies and their possible impact.
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5. Finally, the utilization and interpretation step involves the use of the map to help address the original focus. On the programme side, the maps can be used as a visual framework to prove the results of the analyses and the reports that the partners have generated or for their possible improvements. The map can be used for developing measures and displaying results and for further analyses and planning when a scenario approach is needed. The interpretation is easy. The empty or low-level evaluated areas, if they are not resulting in a lack of competences and information, will generate opportunities for the business to proactive product/services development, investing in R&D and for upskilling new talents.

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Author
KISMC

This article is animated by the interim results of an ongoing Erasmus+ project named Smart technologies by design (Smart by Design).

The project is being carried out by the following partners:

• KISMC - an innovation management organization that is focused on developing competences in innovation, creativity and entrepreneurship
• GAIA - a cluster & BSO that unites companies from the knowledge and applied technologies industries and supports policy and deployment of ICT, Engineering and Electronics in Basque Country
• DEUSTO - leading university in Spain specialized in educating & training in the innovation and entrepreneurship, design thinking and IoT & Smart city solutions through its Business School and the Faculty of Engineering
• United Academics - Foundation in Netherlands that promotes, supports and maintains open-access library and publishing that results in faster scientific communication, wider influences of scientific knowledge on the industry, government, and education
• ARIES - industrial cluster and BSO that contributes to designing & implementing the smart city strategy of Cluj in Romania, supports digital transformation and creates digital innovation hubs
• ULSIT - Bulgarian state university of bibliography and information technology.  
  
  

In the next several articles the project partners will present their findings and deliverables generated and developed during the project execution. Our attention will be focused on the gap between the citizen's (smart cities') needs, the technological solutions which can cover these needs, and the knowledge and skills of citizens, cities and service providers of using these technologies for a better life.
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Disruptive technologies have the potential to transform the way cities currently operate and they are at the core of nearly all upcoming smart city’s solutions. After a decade of experimentation, smart cities are entering a new phase. Although digital solutions are only one of the tools needed to make a city great, they are the most powerful and cost-effective additions seen in many years.

According to the McKinsey Global Institute, digital solutions could improve some quality-of-life indicators by as much as 30 percent. Real-time crime mapping, for instance, utilizes statistical analysis to highlight patterns, while predictive policing goes a step further, anticipating crime to head off incidents before they occur. Another example of these solutions is the Internet of Things sensors on existing infrastructure systems which can help crews fix problems before they turn into breakdowns and delays.
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If we examine our history, we realize that we live in constant change. Humans have faced all sorts of changes, economic, political, climatic, technological. In the different industrial revolutions seized, adaptations made by humans can be perceived, along the 1st industrial revolution, railway arrived with the steam engines which enabled transportation of goods causing all the farming, demographic and transport revolution.

The city of the future must meet the needs of its residents. Yet in surveying residents of 25 major cities, McKinsey finds that a fifth of those cities falls short of delivering satisfaction. Respondents cited numerous inadequacies: crime, congestion, fire emergency response, waste management, active mobility options, police security, lack of basic utilities, public transit, as well as poor quality of housing and government services. Given the fierce competition for talent across cities, dissatisfied urbanites are likely to vote with their feet and leave for more attractive environments. (Source: Thriving amid turbulence: Imagining the cities of the future; Capital Projects & Infrastructure, Public Sector October 2018, McKinsey & Company).

In order to not lag behind due to inactivity, the city leaders must know how to use the newest and most advanced technologies, which is progressing faster than expected.
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We have realized during the implementation of the project and as a result of the study that the most important and those with enough predictive potential to transform the cities into smart cities are the following technologies (technology areas):

1. Artificial Intelligence
2. Data analytics
3. Cloud Computing
4. Internet of Things
5. Cyber physical systems
6. Smart sensors
7. Collaborative robotics
8. Cybersecurity
9. Blockchain
10. Augmented reality
11. Virtual reality

We start with the first area - Artificial Intelligence (AI) and below you can find the features, functionalities, existing platforms, and standards, which are recognized by us as the most important from the point of view of upcoming evolving and disruptive solutions in the smart cities.

Artificial Intelligence (AI) refers to the technology which aims to create intelligent machines that react and work like humans. Some of the skills for the computers with AI are the speech recognition, learning (information acquisition and patterns to use it), reasoning (using rules to reach definitive conclusions), self-correction and problems solving.

This technology allows machines to learn from past experiences, adjust to new inputs and perform tasks like humans. Nowadays, the AI refers to a big range of concepts, from robotic process automation to the current more sophisticated robots. It has evolved due to the huge amount of available data or the increase of speed, size and variety of data that companies are collecting. The AI is also able to realize tasks such as the identification of patterns in data in a much more efficient way than humans, which allows companies to extract better conclusions from the information.

 The Artificial Intelligence can be categorized in different ways and types of activities that they perform . But basically, the tasks that a computer based on AI can carry out, can go from developed very concrete tasks (faint AI) to other systems provided with human cognitive skills (strong AI), being able to give answers to problems or tasks that previously were not assigned or expected to them.
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In recent years, AI has grown capabilities thanks to the advance of some technologies, such as: 

• Improved machine learning (ML) techniques
• Availability of massive amounts of training data
• Unprecedented computing power
• Mobile connectivity

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Platforms for AI are hardware architectures or software frameworks that allow the software to run. Even if there are many different types of platforms related to this technology, some of the most relevant are: 

• Microsoft Azure Machine Learning
• Google Cloud Prediction API
• TensorFlow
• Infosys Nia
• Wipro Homes
• API.AI
• Premonition
• Rainbird         
• Ayasdi
• MindMeld
• Wit
• Vital A.I
• KAI
• Receptiviti
• Rage-A
• Infrrd

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The European Commission has launched the Communication COM (2018) Artificial Intelligence for Europe which establishes a new initiative for Europe about AI. This recognizes the need of the standardization as an answer to the challenges of this key technology, especially in terms of security, trust and ethical considerations. CEN and CENELEC support the fulfillment of the European legislation with harmonized standards.

In relation to international organizations, the ISO has a technical committee which is working on the development of standards in Artificial Intelligence, which is the ISO/IEC JTC 1/SC 42. There are currently two published ISO standards under this working group, and there are some more which are being developed.

Published standards:

• ISO/IEC TR 20547-2:2018: Information technology - Big data reference architecture - Part 2: Use cases and derived requirements;
• ISO/IEC TR 20547-5:2018: Information technology - Big data reference architecture - Part 5: Standards roadmap;

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Artificial Intelligence is a term which has existed for many years, but due to the development that current technologies are having, it has been recently spread to different application sectors and environments. Some of them are:

• Entertainment: videogames, apps, sports betting;
• Recommendations in music, videos or movies;
• Self-driving cars like Google driverless car, Tesla’s autopilot: self-parking, collision detection, blind spot monitoring, voice recognition or navigation;
• Chat-bots for online customer service;
• Banking and finance: analysing market data, manage finances, offer suggestions;
• Manufacturing: assembling plants;
• eCommerce and digital marketing;
• Home applications: learning behaviours and patterns;
• Electronics: thermostats or smart lights;
• Workplace communication;
• Healthcare: diagnosis and treatments, virtual nurses;
• Cybersecurity;
• Logistics and supply chain;
• Online retail stores;
• Smartphones: virtual personal assistants (Siri, Cortana or Google Now)

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​As one of the main technologies which will help to evolve our ways to work and interact with technology, the trend is that the AI will keep growing and becoming more normalized in our society. So, platforms, standards, and applications will keep incrementing. 

Apparently, the future of the AI resides on a deeper personalization, innovation in voice AI and a better view of the customer. Even if there are plenty of platforms for AI, not many of them are deploying voice interfaces. Some applications for mobile devices have already implemented it in daily life and it will offer opportunities for companies. The tendency will be to add voice control to their AI for a better experience with their customers. Some of the most famous firms (Google, Amazon...) are integrating voice assistants as part of their services.
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The AI will help for a bigger personalization of services. Customers will be able to express preferences to the biggest brands and acquire much more personalized services or products. So, future platforms will shift towards a new type of services. 

The European Commission foresees that Artificial Intelligence will impact the commitment of some European guidelines. It will have effects in several sectors in which standardization is very relevant: smart manufacturing, robotics, autonomous driving, virtual reality, health sector, visual recognition, data analysis and management, domestic tools or cybersecurity. In all those sectors there are already essential standards which will need to be updated in order to add this new technology.

Currently, there are four standards under development from the working group ISO/IEC JTC 1/SC 42:

• ISO/IEC AWI TR 20547-1: Information technology -- Big data reference architecture Part 1: Framework and application process;
• ISO/IEC DIS 20547-3:  Information technology -- Big data reference architecture -- Part 3: Reference architecture;
• ISO/IEC AWI 22989: Artificial Intelligence Concepts and Terminology
• ISO/IEC AWI 23053: Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML).

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Even if Artificial Intelligence is becoming more common in our daily life, there are many more applications foreseen or improved such as:

• Automated transport: fully self-driving cars, buses or trains;
• Cyborg technology: enhancing natural abilities, amputated parts;
• Climate change: identifying trends and use information to come up with solutions and natural disasters;
• Dangerous jobs: bomb defusing, toxic substances, intense heat, difficult access places, prevent human harm;
• Robot as friends: in the future, robots will be able to understand and feel emotions;
• Elder care: helping with everyday life providing more independence;
• Journalism: it is expected that the AI will be able to write articles or reports which don’t require a very deep knowledge.

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